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At least 703 records · Page 39

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NASA NTRSDate not supplied

A Comparison of Model Predictive Control Architectures for Application to Electrified Aircraft Propulsion Systems

As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.

model predictive control↗

NASA NTRSDate not supplied

Database Design Strategies for Coordinated Simulation and Testing in Additive Manufacturing

The qualification and certification (Q&C) process presents a significant challenge for widespread adoption of additive manufacturing (AM) materials and processes for aerospace applications. A relational database framework will be presented as a tool for data curation of coordinated experimental and computational materials modeling research activities. A comparison of relational and hierarchical data structures in this domain will be emphasized through the evolution of a database design strategy. This framework’s mission is to support the advancement of computational materials-informed Q&C by providing the necessary data infrastructure to trace reliability and reproducibility measures through unified AM materials simulation and experimental testing. FAIR (findable, accessible, interoperable, and reusable) data will be highlighted as a necessary precursor for automation of specific actions, which ultimately reduces the time and expense burden for Q&C. The discussion will be mostly limited to back-end design elements, though a few front-end user experience examples will also be shared.

Qualification↗

NASA NTRS2004

Unsteady Analyses of Valve Systems in Rocket Engine Testing Environments

This paper discusses simulation technology used to support the testing of rocket propulsion systems by performing high fidelity analyses of feed system components. A generalized multi-element framework has been used to perform simulations of control valve systems. This framework provides the flexibility to resolve the structural and functional complexities typically associated with valve-based high pressure feed systems that are difficult to deal with using traditional Computational Fluid Dynamics (CFD) methods. In order to validate this framework for control valve systems, results are presented for simulations of a cryogenic control valve at various plug settings and compared to both experimental data and simulation results obtained at NASA Stennis Space Center. A detailed unsteady analysis has also been performed for a pressure regulator type control valve used to support rocket engine and component testing at Stennis Space Center. The transient simulation captures the onset of a modal instability that has been observed in the operation of the valve. A discussion of the flow physics responsible for the instability and a prediction of the dominant modes associated with the fluctuations is presented.

Shipman, Jeremy↗

NASA NTRS2010

Kepler Science Operations Center Pipeline Framework

The Kepler mission is designed to continuously monitor up to 170,000 stars at a 30 minute cadence for 3.5 years searching for Earth-size planets. The data are processed at the Science Operations Center (SOC) at NASA Ames Research Center. Because of the large volume of data and the memory and CPU-intensive nature of the analysis, significant computing hardware is required. We have developed generic pipeline framework software that is used to distribute and synchronize the processing across a cluster of CPUs and to manage the resulting products. The framework is written in Java and is therefore platform-independent, and scales from a single, standalone workstation (for development and research on small data sets) to a full cluster of homogeneous or heterogeneous hardware with minimal configuration changes. A plug-in architecture provides customized control of the unit of work without the need to modify the framework itself. Distributed transaction services provide for atomic storage of pipeline products for a unit of work across a relational database and the custom Kepler DB. Generic parameter management and data accountability services are provided to record the parameter values, software versions, and other meta-data used for each pipeline execution. A graphical console allows for the configuration, execution, and monitoring of pipelines. An alert and metrics subsystem is used to monitor the health and performance of the pipeline. The framework was developed for the Kepler project based on Kepler requirements, but the framework itself is generic and could be used for a variety of applications where these features are needed.

Klaus, Todd C.↗

NASA NTRS2018

Blunt-Body Paradox and Improved Application of Transient-Growth Framework

The “Reshotko-Tumin transition criterion" based on optimal transient growth successfully correlates laboratory measurements of roughness induced transition over blunt body configurations. Even though transient growth has not been conclusively linked to the measured onset of transition, the above correlation denotes the only available physics-based model for subcritical transition in blunt body flows, since the latter do not support any modal instabilities at typical experimental conditions. Unlike other established models based on empirical curve fits that are valid for a specific subclass of datasets, the optimal-growth-based transition criterion appears to provide a reasonable correlation with measurements in various wind tunnel and ballistic range facilities and for a broad range of surface temperature ratios. This paper is focused on optimal growth calculations that improve upon significant shortcomings of the computations underlying the Reshotko-Tumin correlation. The improved framework is applied to leeward transition over a spherical section forebody that was tested in the Mach 6 Adjustable Contour Expansion wind tunnel at Texas A&M University. The computed results highlight the significance of nonparallel basic state evolution, curvature terms, and an optimization procedure that varies both inflow and outflow locations of the transient growth interval. More important, the results indicate that the modified correlation is very close to its original form, and hence, that the accuracy of the transient-growth-based transition criterion is not compromised by using a more thorough theoretical framework. Yet the results also show that the optimal energy gain up to the predicted transition onset location can be rather small, highlighting the need to further investigate the optimal growth criterion for additional experimental configurations and to also uncover the in-depth physics underlying blunt body transition.

Freestream conditions↗

NASA NTRS2005

Support for User Interfaces for Distributed Systems

An extensible Java(TradeMark) software framework supports the construction and operation of graphical user interfaces (GUIs) for distributed computing systems typified by ground control systems that send commands to, and receive telemetric data from, spacecraft. Heretofore, such GUIs have been custom built for each new system at considerable expense. In contrast, the present framework affords generic capabilities that can be shared by different distributed systems. Dynamic class loading, reflection, and other run-time capabilities of the Java language and JavaBeans component architecture enable the creation of a GUI for each new distributed computing system with a minimum of custom effort. By use of this framework, GUI components in control panels and menus can send commands to a particular distributed system with a minimum of system-specific code. The framework receives, decodes, processes, and displays telemetry data; custom telemetry data handling can be added for a particular system. The framework supports saving and later restoration of users configurations of control panels and telemetry displays with a minimum of effort in writing system-specific code. GUIs constructed within this framework can be deployed in any operating system with a Java run-time environment, without recompilation or code changes.

Eychaner, Glenn↗

NASA NTRSDate not supplied

A Design Framework for Thick and Thin Tow-Steered Composites Using Mechanics of Structure Genome

The design of tow-steered composites is one of the most popular and promising topics under the class of variable stiffness structures. In this work, the authors propose a new design framework and tool for the optimization of tow-steered composites. Mechanics of structure genome (MSG) provides accurate computation of plate/shell section properties including thick and highly curved structures. The computed section properties can be directly used in commercial computer-aided engineering tools. Tow-steered composites are modeled through a design framework wherein tow paths are parameterized in a general way and projected onto a finite element mesh, and MSG calculates the local shell/plate properties. The result is a complete workflow from design parameter input to structural performance evaluation. An open-source optimization software package is used to enable constrained optimization of structural stiffness for the broad tow-steered composite design space. Numerical examples are provided to demonstrate the capabilities of this tool and the promise and potential of tow-steered designs that optimize structural performance while satisfying manufacturing constraints.

Su Tian↗

NASA NTRSDate not supplied

A Design Framework for Thick and Thin Tow-Steered Composites Using Mechanics of Structure Genome

Su Tian↗

DOE OSTI2026

Resilience Metrics Framework for Solar Photovoltaics

This presentation was given at the Photovoltaic Specialist Conference (PVSC) 54 in New Orleans, Louisiana. Photovoltaic (PV) systems are routinely exposed to extreme weather, including wind and hail storms. Historically, most systems have proven to be resilient to such events, but some storms have damaged PV systems, leading to physical and financial loss. Storm hardening measures and specific system attributes can reduce this risk. This work introduces a set of resilience metrics and a framework for quantifying, comparing, and predicting PV system resilience. The framework is divided into two parts: 1) predictive, attribute metrics based on site and component characteristics, and 2) impact metrics that assess post-storm performance. Metrics are weighted and aggregated, producing hazard-specific resilience scores. We derive damage functions from storm-impacted PV systems, establishing a baseline against which post-storm performance can be compared. This damage was widely variable across hail and wind intensities, and field hail damage was less than predicted by laboratory tests, suggesting that system features - in addition to storm conditions - influence damage likelihood. Finally, the metrics framework is demonstrated using three case studies of storm damaged PV systems. Although additional data are needed to create attribute specific damage functions and establish metric weights, this study presents a methodology for evaluating PV resilience and contributes new damage functions to the literature.

14 SOLAR ENERGY↗

NASA NTRS2003

Computation of Domain-Averaged Shortwave Irradiance by a One-Dimensional Algorithm Incorporating Correlations between Optical Thickness and Direct Incident Radiation

A one-dimensional radiative transfer algorithm that accounts for correlations between the optical thickness and the incident direct solar radiation is developed to compute the domain-averaged shortwave irradiance profile. It divides the direct irradiance into four components and treats the direct irradiance in two separate, clear and cloudy columns to account for the fact that clouds attenuate the direct irradiance more than clear-sky. The horizontal inhomogeneity of clouds in the cloudy column is treated by the gamma weighted two-stream approximation, which assumes that the optical thickness of clouds follows a gamma distribution. The algorithm inputs the cloud fraction, cumulative cloud fraction as a function of height, and a parameter expressing the shape of the probability density function of the cloud optical thickness distribution in addition to inputs required for a two-stream radiative transfer model. These cloud property inputs can be obtained using ground- and satellite-based instruments. Therefore, the algorithm can treat realistic cloud overlap features and horizontal inhomogeneity of clouds in a framework of one- dimensional radiative transfer. Heating rates computed by the algorithm using cloud fields generated by cloud resolving models agree with those computed with a Monte Carlo model. If optical properties in computational layers that divide a vertically extensive cloud are correlated, the irradiance profile computed by the algorithm further improves.

Kato, S.↗

DOE OSTI2025

Gaussian Process Regression under Computational and Epistemic Misspecification

Gaussian process regression is a classical kernel method for function estimation and data interpolation. In large data applications, computational costs can be reduced using low-rank or sparse approximations of the kernel. This paper investigates the effect of such kernel approximations on the interpolation error. We introduce a unified framework to analyze Gaussian process regression under important classes of computational misspecification: Karhunen-Loève expansions that result in low-rank kernel approximations, multiscale wavelet expansions that induce sparsity in the covariance matrix, and finite element representations that induce sparsity in the precision matrix. Furthermore, our theory also accounts for epistemic misspecification in the choice of kernel parameters.

Gaussian process regression↗

DOE OSTI2026

Lessons Learned from Open-Source Software Training Toward Expanding the Fusion Workforce

Within the United States (U.S.), the Fusion Innovation Research Engine (FIRE) Collaboratives seek to accelerate fusion technology development through wide-ranging community-driven research activities that bring together industry, laboratories, research institutes, and academia. Increasing the maturity of key components and systems for future fusion power plants (FPPs) toward commercialization, will, by necessity, increase the need for knowledgeable engineers, designers, and researchers in industry capable of integrating and further improving these technologies within industry FPP concepts. Further, various modeling and simulation packages support these programs and are under-development within them to drive design iteration and the development of FPP digital twins. To derive the greatest benefit from model output and capabilities, training these same specialists will be vital. Thus, the development of effective and accessible software onboarding and professional development opportunities focused on fusion will be key to keeping the pace of growth high in the coming decade and beyond. A similar need exists within the advanced fission reactor community, driven by an ever-growing need for carbon-free baseload power for industrial and data center applications. Within the U.S. and around the world, several open-source packages and frameworks exist to support this endeavor, and two we will highlight here are the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and OpenMC. These packages, notably, are also being utilized in the FIRE Collaboratives program. In this presentation, we will highlight the lessons learned from over 30 years of combined software development and training experience, focused on developing strong technical software foundations within the nuclear workforce, from students to professional engineers & scientists. We will connect this experience to present and emerging needs in the fusion energy community, and, finally, will outline possible paths forward to develop a large, robust, global fusion workforce.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Molecular Recognition with Resolution Below 0.2 Angstroms Through Thermoregulatory Oscillations in Covalent Organic Frameworks

Crystalline materials with uniform molecular-sized pores are desirable for a broad range of applications, such as sensors, catalysis, and separations. However, it is challenging to tune the pore size of a single material continuously and to reversibly distinguish small molecules (below 4 angstroms). We synthesized a series of ionic covalent organic frameworks using a tetraphenoxyborate linkage that maintains meticulous synergy between structural rigidity and local flexibility to achieve continuous and reversible (100 thermal cycles) tunability of “dynamic pores” between 2.9 and 4.0 angstroms, with resolution below 0.2 angstroms. This results from temperature-regulated, gradual amplitude change of high-frequency linker oscillations. These thermoelastic apertures selectively block larger molecules over marginally smaller ones, demonstrating size-based molecular recognition and the potential for separating challenging gas mixtures such as oxygen/nitrogen and nitrogen/methane.

crystalline materials↗

DOE OSTI2026

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating a closed-form predictive distribution over outputs informed by non-identically distributed training data. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example out-of-training-sample. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy—referred to as information sharing—that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

17 WIND ENERGY↗

DOE OSTI2026

Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute

As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Measurements over distributed high performance computing and storage systems

A strawman proposal is given for a framework for presenting a common set of metrics for supercomputers, workstations, file servers, mass storage systems, and the networks that interconnect them. Production control and database systems are also included. Though other applications and third part software systems are not addressed, it is important to measure them as well.

Williams, Elizabeth↗

DOE OSTI2024

An Open-Source Parallel EMT Simulation Framework

As the integration level of inverter-based resources (IBRs) increases, ensuring the reliable operation of the bulk power systems requires the use of electromagnetic transient (EMT) simulation tools to identify and mitigate system-wide stability risks. Conducting EMT studies for large-scale, IBR-rich grids, however, is challenging due to the inherent computational bottleneck caused by the underlying high-fidelity models and required small time steps. This paper introduces ParaEMT: an open-source, generic EMT simulation framework designed to accelerate simulations by leveraging advanced parallel computational technologies, such as high-performance computers. This paper presents a comprehensive exposition of ParaEMT, covering its modeling library, simulation strategy, framework structure, operational procedures, and auxiliary features, alongside its extensible parallel computational architecture. Notably, ParaEMT is a publicly accessible and modularized framework written in Python, thereby facilitating future development and the integration of new models and algorithms. The accuracy and efficiency of ParaEMT are demonstrated by rigorous validations via multiple case studies.

electromagnetic transient simulation↗

NASA NTRS2000

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.

Johnston, William E.↗